• The Korean Journal of Mycology
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• 제10권4호
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• pp.233-236
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• 1982

• The monthly packaging world
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• 통권271호
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• pp.74-83
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• 2015

• Proceedings of the International Microelectronics And Packaging Society Conference
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• 한국마이크로전자및패키징학회 2003년도 국제표면실장 및 인쇄회로기판 생산기자재전:전자패키지기술세미나
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• pp.93-114
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• 2003

• Journal of Institute of Control, Robotics and Systems
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• 제21권12호
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• pp.1178-1184
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• 2015

In conventional robotics, charge-coupled device (CCD) and complementary metal-oxide-semiconductor (CMOS) cameras have been utilized for acquiring vision information. These devices have problems, such as narrow optic angles and inefficiencies in visual information processing. Recently, biomimetic vision sensors for robotic applications have been receiving much attention. These sensors are more efficient than conventional vision sensors in terms of the optic angle, power consumption, dynamic range, and redundancy suppression. This paper presents recent research trends on biomimetic vision sensors and discusses future directions.

• World Technopolis Review
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• 제5권1호
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• pp.47-60
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• 2016

In today's digital non-linear global business environment, innovation initiatives are influenced by inter-organizational, political, economic, environmental, technological systems, as well as by decisions made individually by key actors in these systems. Network-based structures emerge from social linkages and collaborations among various actors, creating innovation ecosystems, complex adaptive systems in which entities co-create value. A shared vision of value co-creation allows people operating individually to arrive together at the same future. Yet, relationships are difficult to see, continually changing and challenging to manage. The Innovation Ecosystem Transformation Framework construct includes three core components to make innovation relationships visible and articulate networks of relational capital for the wellbeing, sustainability and business success of innovation ecosystems: data-driven visualizations, storytelling and shared vision. Access to data facilitates building evidence-based visualizations using relational data. This has dramatically altered the way leaders can use data-driven analysis to develop insights and provide ongoing feedback needed to orchestrate relational capital and build shared vision for high quality decisions about innovation. Enabled by a shared vision, relational capital can guide decisions that catalyze, support and sustain an ecosystemic milieu conducive to innovation for business growth.

• Transactions of the Korean Society of Automotive Engineers
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• 제12권3호
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• pp.184-191
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• 2004

In this study, an inspection system for automotive parts using machine vision has been developed and presented. The system is comprised of six analog CCD cameras, frame grabber, and mechanism that loads the automotive parts to the system for the inspection. An Image processing algorithm for detecting eight different types of defects of oil-seals are developed, and the effectiveness of the algorithm is experimentally verified. Inspection process is completed in 1 second with acceptable accuracy. It is envisaged that this inspection system will have a wide application in the automotive part manufacturing industry in the future.

• The Research Journal of the Costume Culture
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• 제10권4호
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• pp.336-349
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• 2002

The purpose of this paper is to examine the characteristics of consumption vision related to the apparel buying decision-making. They propose that consumers form mental images of future consumption situations and that these consumption visions influence their decision-making. Consumers can imagine themselves consuming apparel products and experiencing the consequences of this consumption. By imagining the likely outcomes, they are able to identify the salient characteristics of each alternative and develop beliefs about their outcomes. Also, they can experience affective reactions to the outcomes they imagines. In this way, they form the cognitive and affective basis for their preferences and construct several consumption visions in the apparel buying decision-making. A consumption vision is "a visual image of certain product-related behaviors and their consequences....(they consisted of concrete and vivid mental images that enable consumers to vicariously experience the self-relevant consequences of product use"(Walker & Olson, 1994). We conducted unstructured, depth interviews with 9 groups participating 48 students at universities located in Busan, based on the results of previous studies. The results show that consumption visions related to the apparel buying decision-making are characterized as self-image, reactions of others, affection and mood, visual imagine, and self-satisfaction. By constructing consumption visions based on the various perspectives, consumers are influenced in the apparel buying decision-making. Many subjects reported experiencing positive affect when imagining positive outcomes of product use. Other subjects mentioned using consumption visions for purely hedonic reasons. With no intention of purchasing apparel products, consumers may evoke consumption visions to escape from the daily life, to fantasize and daydream about pleasurable consumption situations, and to enhance the mood. That is, the consumption vision related to the apparel buying decision-making helps consumers anticipate an uncertain future and make the purchase of apparel products.

• International Journal of Advanced Culture Technology
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• 제9권3호
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• pp.174-180
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• 2021

The necessity of this study is as follows. A decrease in the number of newborns, an increase in the youth unemployment rate, and a decrease in the employment rate are having a fatal impact on universities. To help increase the employment rate of universities, we intend to utilize Big Data of university public information. Big data refers to the process of collecting and analyzing data, and includes all business processes of finding data, reprocessing information in an easy-to-understand manner, and selling information to people and institutions. Big data technology can be divided into technologies for storing, refining, analyzing, and predicting big data. The purpose of this study is to find the vision and special department of a university with a high employment rate by using big data technology. As a result of the study, big data was collected from 227 universities on www.academyinfo.go.kr site, We selected 130 meaningful universities and selected 25 universities with high employment rates and 25 universities with low employment rates. In conclusion, the university with a high employment rate can first be said to have a student-centered vision and university specialization. The reason is that, for universities with a high employment rate, the vision was to foster talents and specialize, whereas for universities with a low employment rate, regional bases took precedence. Second, universities with a high employment rate have a high interest in specialized departments. This is because, as a result of checking the presence or absence of a characterization plan, universities with a high employment rate were twice as high (21/7). Third, universities with high employment rates promote social needs and characterization. This is because the characteristic departments of universities with high employment rates are in the order of future technology and nursing and health, while universities with low employment rates promoted school-centered specialization in future technology and culture, tourism and art. In summary, universities with high employment rates showed high interest in student-centered vision and development of special departments for social needs.

• Journal of English Language & Literature
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• 제58권1호
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• pp.43-67
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• 2012

My essay aims at illustrating Whitman's homosexual vision of utopia with a close reading of his representative homosexual text, Calamus. His expansive self is based upon his intimate contact with the world and is almost always drawn to a wider vision of community in which different individuals share the locus of commonness and reach beyond their empirical boundaries. While foregrounding the contingent and the singular, Whitman forges bonds with other people through a series of ecstatic moments that carry us into the public sphere and common interests. Contrary to the current Whitman studies, his homosexual text doesn't repress contingency in order to celebrate the universal, but fully develops the commensurability among diverse historical agents. Whitman knows well the social taboos and inhibitions at the time of national crisis and expansion, but keeps imagining the world where homosexuality plays a central and significant role in founding a democratic solidarity and achieving a desirable social structure. His ideal of America is not a deferred wish for the future, but a concrete vision that can be achieved here and now, realized by the spontaneous bonding and instant attraction among free men. Instead of interpreting history or suggesting practical alternatives, he keeps questioning the dominant ideologies and the given orders of social control, and suggests a free and open relationship among men where no exterior power or mediating other intervenes. His utopian vision is radical as well as ideal, in that it rejects the interventions of the power structure and its institutions and courageously inscribes his homosexuality in the process of writing about and reading his contemporary America. As a predecessor of a homosexual utopian vision of America, Whitman has inspired many later poets, showing a possibility of infusing a homosexual identity into a radical imaging of the nation and its future.

• Proceedings of the Korean Society of Computer Information Conference
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• 한국컴퓨터정보학회 2023년도 제67차 동계학술대회논문집 31권1호
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• pp.9-12
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• 2023

최근 영상 콘텐츠를 통해 영상물의 메시지뿐 아니라 메시지의 형식을 통해 전달된 감정이 시청하는 사람의 심리 상태에 영향을 주고 있다. 이에 따라, 영상 콘텐츠의 감정을 분류하는 연구가 활발히 진행되고 있고 본 논문에서는 대중적인 영상 스트리밍 플랫폼 중 하나인 유튜브 영상을 7가지의 감정 카테고리로 분류하는 여러 개의 영상 데이터 중 각 영상 데이터에서 오디오와 이미지 데이터를 각각 추출하여 학습에 이용하는 멀티 모달 방식 기반의 영상 감정 분류 모델을 제안한다. 사전 학습된 VGG(Visual Geometry Group)모델과 ViT(Vision Transformer) 모델을 오디오 분류 모델과 이미지 분류 모델에 이용하여 학습하고 본 논문에서 제안하는 병합 방법을 이용하여 병합 후 비교하였다. 본 논문에서는 기존 영상 데이터 감정 분류 방식과 다르게 영상 속에서 화자를 인식하지 않고 감정을 분류하여 최고 48%의 정확도를 얻었다.